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Bio-inspired Assurance and Regression Testing to Secure Organic Programs

Software Engineering needs to address an increasingly significant class of programs that are self-adaptive and self-healing. These programs sense changes to their environment and react by modifying configurations, libraries or program code. Furthermore, automated approaches for program repair and program transplantation change a program's source code directly to fix, optimize or add new functionality. Together, self-modification provides continual availability in the presence of change and can harden a system against intruders. While this organic nature of self-modification is a powerful paradigm, the overall dependability and security of such programs is at risk.  This project draws inspiration from nature and uses bio-inspired techniques to design testing techniques on these programs. Both traditional functional faults as well as security vulnerabilities are targeted.

National Science Foundation   CCF#1901543

Recent Publications:

2.  M. Cashman,  J. Firestone, M.B. Cohen, T. Thianniwet, W. Niu, An empirical investigation of organic software product lines. Empirical Software Engineering 26, 44 (2021).  [bibtex/abstract] (open access)

1.  M. Cashman, J. Firestone, M. B. Cohen, T. Thianniwet, W. Niu, DNA as Features: Organic Software Product Lines, International Systems and  Software Product Line Conference, Sep., 2019, pp.. Best Student Paper Award

3.  J. L. Catlett, J. Catazaro, M. Cashman, S. Carr, R. Powers, M. B. Cohen, N. R. Buan ,Metabolic
Feedback Inhibition Influences Metabolite Secretion by the Human Gut Symbiont Bacteroides thetaiotaomicron,
American Society for Microbiology, mSystems, 5(5), October, pp. 1-16, 2020.

4.   U. Sinha, M. Cashman, M.B. Cohen, Using a Genetic Algorithm to Optimize Configurations in a Data-Driven Application, International Symposium on Search Based Software Engineering, September 2030, pp. 137-152.

5.  M. Cashman, M. B. Cohen, P. Ranjan, R. W. Cottingham, Navigating the Maze: the Impact of Configurability in Bioinformatics Software, IEEE/ACM International Conference on Automated Software Engineering (ASE), Sept, 2018, pp. 757-767, ACM distinguished paper award.